A Comprehensive Study of Activity Recognition Using Accelerometers
This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there...
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doaj-87177d4613a14330a5ce4b4461a56fee2020-11-24T22:30:31ZengMDPI AGInformatics2227-97092018-05-01522710.3390/informatics5020027informatics5020027A Comprehensive Study of Activity Recognition Using AccelerometersNiall Twomey0Tom Diethe1Xenofon Fafoutis2Atis Elsts3Ryan McConville4Peter Flach5Ian Craddock6School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UKSchool of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UKSchool of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UKSchool of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UKSchool of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UKSchool of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UKSchool of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, University of Bristol, Bristol BS8 1UB, UKThis paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there are performance trade-offs between prediction accuracy, which is not the sole system objective, and keeping the maintenance overhead at minimum levels. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions.http://www.mdpi.com/2227-9709/5/2/27activities of daily livingactivity recognitionaccelerometersmachine learningsensors |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Niall Twomey Tom Diethe Xenofon Fafoutis Atis Elsts Ryan McConville Peter Flach Ian Craddock |
spellingShingle |
Niall Twomey Tom Diethe Xenofon Fafoutis Atis Elsts Ryan McConville Peter Flach Ian Craddock A Comprehensive Study of Activity Recognition Using Accelerometers Informatics activities of daily living activity recognition accelerometers machine learning sensors |
author_facet |
Niall Twomey Tom Diethe Xenofon Fafoutis Atis Elsts Ryan McConville Peter Flach Ian Craddock |
author_sort |
Niall Twomey |
title |
A Comprehensive Study of Activity Recognition Using Accelerometers |
title_short |
A Comprehensive Study of Activity Recognition Using Accelerometers |
title_full |
A Comprehensive Study of Activity Recognition Using Accelerometers |
title_fullStr |
A Comprehensive Study of Activity Recognition Using Accelerometers |
title_full_unstemmed |
A Comprehensive Study of Activity Recognition Using Accelerometers |
title_sort |
comprehensive study of activity recognition using accelerometers |
publisher |
MDPI AG |
series |
Informatics |
issn |
2227-9709 |
publishDate |
2018-05-01 |
description |
This paper serves as a survey and empirical evaluation of the state-of-the-art in activity recognition methods using accelerometers. The paper is particularly focused on long-term activity recognition in real-world settings. In these environments, data collection is not a trivial matter; thus, there are performance trade-offs between prediction accuracy, which is not the sole system objective, and keeping the maintenance overhead at minimum levels. We examine research that has focused on the selection of activities, the features that are extracted from the accelerometer data, the segmentation of the time-series data, the locations of accelerometers, the selection and configuration trade-offs, the test/retest reliability, and the generalisation performance. Furthermore, we study these questions from an experimental platform and show, somewhat surprisingly, that many disparate experimental configurations yield comparable predictive performance on testing data. Our understanding of these results is that the experimental setup directly and indirectly defines a pathway for context to be delivered to the classifier, and that, in some settings, certain configurations are more optimal than alternatives. We conclude by identifying how the main results of this work can be used in practice, specifically in experimental configurations in challenging experimental conditions. |
topic |
activities of daily living activity recognition accelerometers machine learning sensors |
url |
http://www.mdpi.com/2227-9709/5/2/27 |
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